# PDF to Markdown for LLMs and RAG (no file retention) (`hushvert/pdf-to-markdown`) Actor

Convert PDF to clean Markdown for LLM context windows, RAG ingestion, and agent pipelines. Keeps multi-column pages in true reading order. Privacy-first: inputs deleted the moment the conversion finishes, outputs within about an hour. Scanned PDFs fail loudly (no OCR); failed runs are never charged.

- **URL**: https://apify.com/hushvert/pdf-to-markdown.md
- **Developed by:** [Nir Diamant](https://apify.com/hushvert) (community)
- **Categories:** AI, Agents, Developer tools
- **Stats:** 2 total users, 1 monthly users, 66.7% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$30.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## PDF to Markdown for LLMs and RAG (no file retention)

Convert a PDF to clean Markdown for LLM context windows, RAG ingestion, and
agent pipelines through a privacy-first conversion API. Give the actor a direct
file URL; get back a .md file in the run's key-value store plus a dataset row
with the metadata, including a direct download link.

### Why this converter

- **Reading order that survives two-column layouts.** The pipeline (a layout
  pass, then structured conversion) keeps multi-column pages in true reading
  order instead of interleaving the columns line by line, which is the failure
  mode that silently poisons RAG chunks.
- **No file retention.** The input is deleted the moment the conversion
  finishes. The output is kept for about an hour, then deleted. Your documents
  are not stored, mined, or used for anything.
- **Honest failures.** Scanned (image-only) PDFs are rejected with an explicit
  `no_ocr` error; no OCR is performed and no charge is made for a failed run.

### How do I convert a PDF to Markdown on Apify?

1. Press **Start** with the prefilled sample, or paste your own direct PDF URL.
2. Wait for the run to finish; a typical document takes 10 to 20 seconds.
3. Download the .md from the run's **Storage** tab (the `OUTPUT` record), or
   follow the `outputUrl` in the dataset row.

### Input

```json
{
  "fileUrl": "https://example.com/paper.pdf"
}
```

- `fileUrl` (required): direct http(s) URL of the PDF, up to 50 MB.
- `fileName` (optional): base name for the output file.
- `maxWaitSeconds` (optional): how long to wait before failing, default 300.

### Output

- The Markdown is stored as the `OUTPUT` record of the run's key-value store
  (the dataset row carries its direct `outputUrl`).
- One dataset row per successful conversion: `pair`, `fileName`,
  `outputFileName`, `inputBytes`, `outputBytes`, `durationSeconds`, `outputUrl`.

### How much does it cost?

$0.03 per successful conversion, platform usage included - the price you see
is the whole price. A failed run (including the explicit `no_ocr` rejection of
scanned PDFs) produces no dataset item and no charge. A free Apify account is
enough to use it; conversions bill against your Apify usage balance.

### Run it from code

JavaScript ([apify-client](https://docs.apify.com/api/client/js/)):

```js
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('hushvert/pdf-to-markdown').call({
  fileUrl: 'https://example.com/paper.pdf',
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0].outputUrl); // direct .md download link, valid ~1 hour
```

Python ([apify-client](https://docs.apify.com/api/client/python/)):

```python
from apify_client import ApifyClient

client = ApifyClient('YOUR_APIFY_TOKEN')
run = client.actor('hushvert/pdf-to-markdown').call(
    run_input={'fileUrl': 'https://example.com/paper.pdf'})
items = client.dataset(run['defaultDatasetId']).list_items().items
print(items[0]['outputUrl'])
```

Plain HTTP (one call, returns the dataset row):

```bash
curl -X POST \
  'https://api.apify.com/v2/acts/hushvert~pdf-to-markdown/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN' \
  -H 'content-type: application/json' \
  -d '{"fileUrl": "https://example.com/paper.pdf"}'
```

### Can I use it from n8n, Make, Zapier, or LangChain?

Yes. Like any Apify actor, it plugs into
[n8n](https://docs.apify.com/platform/integrations/n8n),
[Make](https://docs.apify.com/platform/integrations/make),
[Zapier](https://docs.apify.com/platform/integrations/zapier), and
[LangChain](https://docs.apify.com/platform/integrations/langchain) through
Apify's official integrations; select the actor by name,
`hushvert/pdf-to-markdown`. AI agents can also call it as a tool through
[Apify's MCP server](https://docs.apify.com/platform/integrations/mcp), which
makes it a natural document-intake step for agent pipelines.

### Is it private?

- The conversion runs on hushvert's server lane, not inside this actor. The
  chain is pdf2docx into a structural pandoc pass (GitHub-flavored Markdown).
  Retention: inputs deleted at conversion end, outputs auto-deleted after about
  an hour.
- Your documents are never stored beyond that window, never mined, and never
  used to train anything.

### FAQ

#### Does it OCR scanned PDFs?

No, and it says so instead of guessing: scanned or image-only PDFs fail loudly
with `no_ocr` and are never charged. What comes back is the PDF's real text,
not a recognition guess.

#### Why does reading order matter for RAG?

A converter that walks a two-column page line by line interleaves the columns:
every chunk you embed mixes two unrelated passages, retrieval quality drops,
and nothing errors. This pipeline runs a layout pass first so the Markdown
follows the page's true reading order.

#### Do tables survive?

Text-based tables come out as Markdown (GFM) tables through the structured
pandoc pass, not as flattened text lines.

#### What is the file size limit?

50 MB per file.

#### What happens when a conversion fails?

The run fails with the converter's real error message, produces no dataset
item, and charges nothing.

### More private converters from hushvert

- [Document to Markdown](https://apify.com/hushvert/document-to-markdown): the
  wider intake funnel - PDF, DOCX, PPTX, XLSX, HTML, EPUB, RST, LaTeX to
  Markdown, inline in the dataset.
- [PDF to DOCX](https://apify.com/hushvert/pdf-to-docx): PDF back to editable
  Word, real text and tables, correct Hebrew/Arabic reading order.
- [DOCX to PDF](https://apify.com/hushvert/docx-to-pdf): Word to PDF.
- [Office to PDF](https://apify.com/hushvert/office-to-pdf): Word, Excel,
  PowerPoint, OpenDocument, RTF, HTML in; PDF out.
- [HTML to PDF](https://apify.com/hushvert/html-to-pdf): real Chromium render,
  paste HTML inline for invoices and reports.
- [Video to MP4](https://apify.com/hushvert/video-to-mp4): MOV, MKV, WEBM, AVI,
  GIF to universal MP4, files up to 500 MB.

### Who is behind this actor?

Built by the hushvert founder. The browser-side conversion engine is MIT open
source (`@hushvert/engine` on npm); formats a browser can convert (images, HEIC,
audio, archives, PDF page ops) are free and client-side at
https://hushvert.com and are deliberately not sold here. Developer API docs:
https://hushvert.com/for-developers

# Actor input Schema

## `fileUrl` (type: `string`):

Direct http(s) download URL of the input file. The file is fetched once, converted, and the input is deleted the moment the conversion finishes.

## `fileName` (type: `string`):

Base name for the converted file. Defaults to the name in the URL.

## `maxWaitSeconds` (type: `integer`):

How long to wait for the conversion before failing. Default 300.

## Actor input object example

```json
{
  "fileUrl": "https://hushvert.com/samples/sample.pdf"
}
```

# Actor output Schema

## `convertedFile` (type: `string`):

The converted file, stored as the OUTPUT record of the run's key-value store.

## `conversions` (type: `string`):

One dataset row per successful conversion: file names, byte counts, duration.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "fileUrl": "https://hushvert.com/samples/sample.pdf"
};

// Run the Actor and wait for it to finish
const run = await client.actor("hushvert/pdf-to-markdown").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "fileUrl": "https://hushvert.com/samples/sample.pdf" }

# Run the Actor and wait for it to finish
run = client.actor("hushvert/pdf-to-markdown").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "fileUrl": "https://hushvert.com/samples/sample.pdf"
}' |
apify call hushvert/pdf-to-markdown --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,hushvert/pdf-to-markdown"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/iFd7GxlCCuZLDbPx0/builds/vqG4qjZxmRrnl4AaX/openapi.json